Plotly Offline¶

Plotly Offline brings interactive Plotly graphs to the offline IPython Notebook environment.

Instead of saving the graphs to a server, your data and graphs will remain inside your IPython notebook. When your ready to share, you can just publish them to the Plotly Cloud or to your company's internal Plotly Enterprise.

To get started with Plotly Offline, start a trial or contact sales@plot.ly to learn about licensing and receive a URL to download the package.

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! pip install plotly --upgrade

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from plotly import __version__
from plotly.offline import download_plotlyjs, init_notebook_mode, iplot

print __version__ # requires version >= 1.7.5

1.7.6

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# download_plotlyjs('your_licensed_plotly_offline_url') # Contact sales@plot.ly to receive a valid URL

Success! Now start an IPython notebook and run the following code to make your first offline graph:

import plotly
plotly.offline.init_notebook_mode() # run at the start of every ipython notebook
plotly.offline.iplot([{"x": [1, 2, 3], "y": [3, 1, 6]}])

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init_notebook_mode() # run at the start of every ipython notebook

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iplot([{"x": [1, 2, 3], "y": [3, 1, 6]}])

Drawing...

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from plotly.graph_objs import *
import numpy as np

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iplot([Box(y = np.random.randn(50), showlegend=False) for i in range(45)])

Drawing...

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x = np.random.randn(2000)
y = np.random.randn(2000)
iplot([Histogram2dContour(x=x, y=y, contours=Contours(coloring='heatmap')),
       Scatter(x=x, y=y, mode='markers', marker=Marker(color='white', size=3, opacity=0.3))])

Drawing...

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# Plotting with Pandas

import pandas as pd
df = pd.read_csv('https://plot.ly/~etpinard/191.csv')
df.head(1)

Out[8]:

	Africa_Life Expentancy [in years]	Africa_text	Africa_marker.size	Africa_Gross Domestic Product per Capita [in USD of the year 2000]	Americas_Life Expentancy [in years]	Americas_text	Americas_marker.size	Americas_Gross Domestic Product per Capita [in USD of the year 2000]	Asia_Life Expentancy [in years]	Asia_text	Asia_marker.size	Asia_Gross Domestic Product per Capita [in USD of the year 2000]	Europe_Life Expentancy [in years]	Europe_text	Europe_marker.size	Europe_Gross Domestic Product per Capita [in USD of the year 2000]	Oceania_Life Expentancy [in years]	Oceania_text	Oceania_marker.size	Oceania_Gross Domestic Product per Capita [in USD of the year 2000]
0	72.301	Country: Algeria <br>Life Expectancy: 72.30...	33333216	6223.367465	75.32	Country: Argentina <br>Life Expectancy: 75....	40301927	12779.37964	43.828	Country: Afghanistan <br>Life Expectancy: 4...	31889923	974.580338	76.423	Country: Albania <br>Life Expectancy: 76.42...	3600523	5937.029526	81.235	Country: Australia <br>Life Expectancy: 81....	20434176	34435.36744

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iplot({
    'data': [
        Scatter(x=df[continent+'_Life Expentancy [in years]'],
                y=df[continent+'_Gross Domestic Product per Capita [in USD of the year 2000]'],
                text=df[continent+'_text'],
                marker=Marker(size=df[continent+'_marker.size'], sizemode='area', sizeref=131868,),
                mode='markers',
                name=continent) for continent in ['Africa', 'Americas', 'Asia', 'Europe', 'Oceania']
    ],
    'layout': Layout(xaxis=XAxis(title='Life Expectancy'), yaxis=YAxis(title='GDP per Capita', type='log'))
})

Drawing...

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# An alternative method for Pandas plotting is with cufflinks: https://github.com/santosjorge/cufflinks
! pip install cufflinks --upgrade

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import cufflinks as cf

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iplot(cf.datagen.lines().iplot(asFigure=True,
                               kind='scatter',xTitle='Dates',yTitle='Returns',title='Returns'))

Drawing...

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iplot(cf.datagen.heatmap(20,20).iplot(asFigure=True,
                                      kind='heatmap',colorscale='spectral',title='Cufflinks - Heatmap'))

Drawing...

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import pandas as pd

df_airports = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')
df_airports.head()

df_flight_paths = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_aa_flight_paths.csv')
df_flight_paths.head()

airports = [ dict(
        type = 'scattergeo',
        locationmode = 'USA-states',
        lon = df_airports['long'],
        lat = df_airports['lat'],
        hoverinfo = 'text',
        text = df_airports['airport'],
        mode = 'markers',
        marker = dict( 
            size=2, 
            color='rgb(255, 0, 0)',
            line = dict(
                width=3,
                color='rgba(68, 68, 68, 0)'
            )
        ))]
        
flight_paths = []
for i in range( len( df_flight_paths ) ):
    flight_paths.append(
        dict(
            type = 'scattergeo',
            locationmode = 'USA-states',
            lon = [ df_flight_paths['start_lon'][i], df_flight_paths['end_lon'][i] ],
            lat = [ df_flight_paths['start_lat'][i], df_flight_paths['end_lat'][i] ],
            mode = 'lines',
            line = dict(
                width = 1,
                color = 'red',
            ),
            opacity = float(df_flight_paths['cnt'][i])/float(df_flight_paths['cnt'].max()),
        )
    )
    
layout = dict(
        title = 'Feb. 2011 American Airline flight paths<br>(Hover for airport names)',
        showlegend = False, 
        height = 900,
        geo = dict(
            scope='north america',
            projection=dict( type='azimuthal equal area' ),
            showland = True,
            landcolor = 'rgb(243, 243, 243)',
            countrycolor = 'rgb(204, 204, 204)',
        ),
    )
    
fig = dict( data=flight_paths + airports, layout=layout )

iplot(fig)

Drawing...

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import plotly.plotly as py # all methods in plotly.plotly will communicate with a Plotly Cloud or Plotly Enterprise

# get_figure downloads a figure from plot.ly or Plotly Enterprise. 
# You need to provide credentials to download figures: https://plot.ly/python/getting-started/
fig = py.get_figure('https://plot.ly/~jackp/8715', raw=True)
iplot(fig)

Drawing...